In the digital era, data has become one of the most critical components of an enterprise. As the volume of data is growing exponentially and data breaches are happening more frequently than ever before, detecting and preventing data loss and controlling data quality has become one of the most pressing security concerns for enterprises.
It is challenging for enterprises to detect data anomaly, protect data against information leakage, and control the data quality in the era of big data. As data becomes one of the most critical components of an enterprise, managing and analyzing large amounts of data provides an enormous competitive advantage for enterprises. However, it also puts sensitive and valuable enterprise data at risk of loss or theft and poses significant security challenges to enterprises. The need to store, process, and analyze more and more data together with the high utilization of modern communication channels in enterprises results in an increase of possible data corruption vectors, including cloud file sharing, email, web pages, instant messaging, FTP (file transfer protocol), removable media/storage, database/file system vulnerability, and social networks.
Data quality control faces the following technical challenges. (1) Completeness: completeness of data refers to whether data has flown correctly through the necessary elements of the IT infrastructure, whether all inputs and transformations happened correctly as prescribed and intended, and whether specific dataflow has been consistently flowing the same path every period the data is delivered. (2) Timeliness: timeliness of data refers to the latency between each transformation in the dataflow and the correct delivery of data at point of receipt according to the pre-specified delivery time. (3) Accuracy: accuracy refers to correct value of data received at the end. Accuracy issues are usually caused by corrupt data in the inputs, or by bugs in data transformation algorithms.
Large organizations usually have a complex network of systems. Over time the systems become so complex that data integrity and quality becomes a huge concern. As data goes through many transformation processes, it is very difficult to backtrack or validate any data coming out in the output stage. Therefore, it is important to develop an IT framework that preserves the quality and integrity of its data residing in various databases across systems and departments.